@article{3406,
  keywords = {ConLSTM, MapReduce, Mean Square Error (MSE), Mean Square Difference (PRD), S-SGD},
  author = {S. Oswalt Manoj and Abhishek Kumar and Ashutosh Kumar Dubey and J. P. Ananth},
  title = {An Adaptive Salp-Stochastic-Gradient-Descent- Based Convolutional LSTM With MapReduce Framework for the Prediction of Rainfall},
  abstract = {Rainfall prediction is considered to be an esteemed research area that impacts the day-to-day life of Indians. The predominant income source of most of the Indian population is agriculture. It helps the farmers to make the appropriate decisions pertaining to cultivation and irrigation. The primary objective of this investigation is to develop a technique for rainfall prediction utilising the MapReduce framework and the convolutional long short-term memory (ConvLSTM) method to circumvent the limitations of higher computational requirements and the inability to process a large number of data points. In this work, an adaptive salp-stochastic-gradientdescent-based ConvLSTM (adaptive S-SGD-based ConvLSTM) system has been developed to predict rainfall accurately to process the long time series data and to eliminate the vanishing problems. To optimize the hyperparameter of the convLSTM model, the S-SGD methodology proposed combine the SGD and the salp swarm algorithm (SSA). The adaptive S-SGD based ConvLSTM has been developed by integrating the adaptive concept in S-SGD. It tunes the weights of ConvLSTM optimally to achieve better prediction accuracy. Assessment measures, such as the percentage root mean square difference (PRD) and mean square error (MSE), were employed to compare the suggested method with previous approaches. The developed system demonstrates high prediction accuracy, achieving minimal values for MSE (0.0042) and PRD (0.8450).},
  year = {9998},
  journal = {International Journal of Interactive Multimedia and Artificial Intelligence},
  volume = {In press},
  chapter = {1},
  number = {In press},
  pages = {1-13},
  month = {01/2024},
  issn = {1989-1660},
  url = {https://www.ijimai.org/journal/sites/default/files/2024-01/ip2024_01_003_0.pdf},
  doi = {10.9781/ijimai.2024.01.003},
}